Mobile communication systems need resource allocation based on accurate channel information in order to support large-capacity multimedia packet services with limited frequency/channel resources. As one of the important channel characteristics representing a channel state, users' velocities are used for efficient management of radio resources. For example, if a mobile communication system is to determine whether to allocate a resource to an MS, it may use the velocity information of the MS to determine whether the MS will stay in its own cell for a long time.
Examples of the conventional MS velocity estimation method include a velocity estimation method based on a covariance function and a velocity estimation method based on an autocorrelation function of a received signal in the time domain.
The velocity estimation based on a covariance function and the velocity estimation method based on an autocorrelation function of a received signal measure the maximum Doppler frequency of a received signal to detect the velocity mapped to the maximum Doppler frequency. That is, the conventional velocity estimation methods estimate the velocity by predicting a change in the covariance function or the autocorrelation function depending on the Doppler spread.
If there is a sufficient measurement time, the above velocity estimation methods can estimate the accurate velocity of an MS regardless of high-speed, medium-speed or low-speed environments. However, time limitations are inevitable in order to measure the MS velocity that varies in real time. The velocity estimation error increases particularly in low-speed environments. Also, the noise influence increases particularly in low-speed environments, thus making it impossible to reliably identify a low-speed MS.
FIG. 1 illustrates a velocity estimation error depending on a Doppler frequency in the conventional art.
Referring to FIG. 1, the axis of abscissas represents a normalized Doppler frequency fdTs and the axis of ordinates represents a means error value NMSE (Normalized Mean Square Error). Herein, fd denotes the maximum Doppler frequency and Ts denotes a sampling period.
The graph of FIG. 1 shows the mean error values estimated using every 1000 samples of a signal with a Signal-to-Noise Ratio (SNR) of 10 dB and a signal with an SNR of 20 dB in Rayleigh Fading environments.
It may be seen from the graph of FIG. 1 that in high 20 dB SNR environments, the mean error is equal to or smaller than 10−1 in a high-speed region of fdTs≧0.05 and the mean error increases suddenly in a low-speed region of fdTs≦0.05. In low 10 dB SNR environments, reliable estimation may be achieved only in a high-speed region of fdTs≧0.1.
In a mobile communication system using a 2 GHz band, the velocity of an MS corresponding to fdTs=0.05 corresponds to 27 km/h in the case of applying a symbol rate of 1 k. Thus, in the case of applying the conventional method, the velocity of an MS in an fdTs≦0.05 region may be inaccurately estimated even when an SNR is sufficiently high (e.g., about 20 dB).
In the conventional art, a large error in the estimation of the velocity of a low-speed MS may be caused by the following three reasons. The first reason is that the inverse of a Bessel function used to estimate the maximum Doppler frequency operates more sensitively in a low-speed region of fdTs≦0.05.
FIG. 2 illustrates a Bessel function and a squared Bessel function according to the conventional art.
Referring to FIG. 2, a change in the estimated fdTs according to a change in the measured mean error value is small in a high-speed fdTs≧0.05 region ‘B’. Alternatively, a change in the estimated fdTs according to a change in the measured mean error value increases greatly in a low-speed fdTs≦0.05 region ‘A’. This phenomenon occurs prominently when an instantaneous change of the measured signal increases due to a reduced measurement time.
The second reason is that the influence of an over bias caused by an additive noise occurs more prominently in the low-speed region. That is, because the additive noise occurs uniformly by the sampling period and the SNR independently of the velocity of the MS, the influence of the additive noise increases in the low-speed region due to the sensitivity to a change of the measured value in the low-speed region.
The third region is that the measured value is distorted by the noise if the SNR value is unknown in the velocity estimation.
FIG. 3 illustrates estimation mean values for an SNR of 10 dB and an SNR of 20 dB according to the conventional art.
Referring to FIG. 3, the axis of abscissas represents a normalized Doppler frequency fdTs and the axis of ordinates represents an estimated Doppler frequency. Herein, fd denotes the maximum Doppler frequency and Ts denotes a sampling period.
It may be seen from the graph of FIG. 3 that an over bias in a low-speed region occurs very seriously if there is an additive noise.
What is therefore required is an apparatus and method for errorlessly estimating the velocity of a low-speed MS in a mobile communication system.